Dataset Information

Assessing the Complex and Evolving Relationship between Charges and Payments in US Hospitals: 1996 - 2012.

ABSTRACT:

Background

In 2013 the United States spent $2.9 trillion on health care, more than in any previous year. Much of the debate around slowing health care spending growth focuses on the complicated pricing system for services. Our investigation contributes to knowledge of health care spending by assessing the relationship between charges and payments in the inpatient hospital setting. In the US, charges and payments differ because of a complex set of incentives that connect health care providers and funders. Our methodology can also be applied to adjust charge data to reflect actual spending.

Methods

We extracted cause of health care encounter (cause), primary payer (payer), charge, and payment information for 50,172 inpatient hospital stays from 1996 through 2012. We used linear regression to assess the relationship between charges and payments, stratified by payer, year, and cause. We applied our estimates to a large, nationally representative hospital charge sample to estimate payments.

Results

The average amount paid per $1 charged varies significantly across three dimensions: payer, year, and cause. Among the 10 largest causes of health care spending, average payments range from 23 to 55 cents per dollar charged. Over time, the amount paid per dollar charged is decreasing for those with private or public insurance, signifying that inpatient charges are increasing faster than the amount insurers pay. Conversely, the amount paid by out-of-pocket payers per dollar charged is increasing over time for several causes. Applying our estimates to a nationally representative hospital charge sample generates payment estimates which align with the official US estimates of inpatient spending.

Conclusions

The amount paid per $1 charged fluctuates significantly depending on the cause of a health care encounter and the primary payer. In addition, the amount paid per charge is changing over time. Transparent accounting of hospital spending requires a detailed assessment of the substantial and growing gap between charges and payments. Understanding what is driving this divergence and generating accurate spending estimates can inform efforts to contain health care spending.

SUBMITTER: Hamavid H

PROVIDER: S-EPMC4938432 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Assessing the Complex and Evolving Relationship between Charges and Payments in US Hospitals: 1996 - 2012.

Hamavid Hannah H Birger Maxwell M Bulchis Anne G AG Lomsadze Liya L Joseph Jonathan J Baral Ranju R Bui Anthony L AL Horst Cody C Johnson Elizabeth E Dieleman Joseph L JL

PloS one 20160708 7

<h4>Background</h4>In 2013 the United States spent $2.9 trillion on health care, more than in any previous year. Much of the debate around slowing health care spending growth focuses on the complicated pricing system for services. Our investigation contributes to knowledge of health care spending by assessing the relationship between charges and payments in the inpatient hospital setting. In the US, charges and payments differ because of a complex set of incentives that connect health care provi ...[more]

PMID: 27390858

Similar Datasets

Project description:BackgroundCigarette smoking is a leading risk factor for morbidity and premature mortality in the United States, yet information about smoking prevalence and trends is not routinely available below the state level, impeding local-level action.MethodsWe used data on 4.7 million adults age 18 and older from the Behavioral Risk Factor Surveillance System (BRFSS) from 1996 to 2012. We derived cigarette smoking status from self-reported data in the BRFSS and applied validated small area estimation methods to generate estimates of current total cigarette smoking prevalence and current daily cigarette smoking prevalence for 3,127 counties and county equivalents annually from 1996 to 2012. We applied a novel method to correct for bias resulting from the exclusion of the wireless-only population in the BRFSS prior to 2011.ResultsTotal cigarette smoking prevalence varies dramatically between counties, even within states, ranging from 9.9% to 41.5% for males and from 5.8% to 40.8% for females in 2012. Counties in the South, particularly in Kentucky, Tennessee, and West Virginia, as well as those with large Native American populations, have the highest rates of total cigarette smoking, while counties in Utah and other Western states have the lowest. Overall, total cigarette smoking prevalence declined between 1996 and 2012 with a median decline across counties of 0.9% per year for males and 0.6% per year for females, and rates of decline for males and females in some counties exceeded 3% per year. Statistically significant declines were concentrated in a relatively small number of counties, however, and more counties saw statistically significant declines in male cigarette smoking prevalence (39.8% of counties) than in female cigarette smoking prevalence (16.2%). Rates of decline varied by income level: counties in the top quintile in terms of income experienced noticeably faster declines than those in the bottom quintile.ConclusionsCounty-level estimates of cigarette smoking prevalence provide a unique opportunity to assess where prevalence remains high and where progress has been slow. These estimates provide the data needed to better develop and implement strategies at a local and at a state level to further reduce the burden imposed by cigarette smoking.

Project description:BackgroundThe Centers for Medicare and Medicaid Services projects that health care costs will continue to grow over the next few years. Rising readmission costs contribute significantly to increasing health care costs. Multiple areas of health care, including readmissions, have benefited from the application of various machine learning algorithms in several ways.ObjectiveWe aimed to identify suitable models for predicting readmission charges billed by hospitals. Our literature review revealed that this application of machine learning is underexplored. We used various predictive methods, ranging from glass-box models (such as regularization techniques) to black-box models (such as deep learning-based models).MethodsWe defined readmissions as readmission with the same major diagnostic category (RSDC) and all-cause readmission category (RADC). For these readmission categories, 576,701 and 1,091,580 individuals, respectively, were identified from the Nationwide Readmission Database of the Healthcare Cost and Utilization Project by the Agency for Healthcare Research and Quality for 2013. Linear regression, lasso regression, elastic net, ridge regression, eXtreme gradient boosting (XGBoost), and a deep learning model based on multilayer perceptron (MLP) were the 6 machine learning algorithms we tested for RSDC and RADC through 10-fold cross-validation.ResultsOur preliminary analysis using a data-driven approach revealed that within RADC, the subsequent readmission charge billed per patient was higher than the previous charge for 541,090 individuals, and this number was 319,233 for RSDC. The top 3 major diagnostic categories (MDCs) for such instances were the same for RADC and RSDC. The average readmission charge billed was higher than the previous charge for 21 of the MDCs in the case of RSDC, whereas it was only for 13 of the MDCs in RADC. We recommend XGBoost and the deep learning model based on MLP for predicting readmission charges. The following performance metrics were obtained for XGBoost: (1) RADC (mean absolute percentage error [MAPE]=3.121%; root mean squared error [RMSE]=0.414; mean absolute error [MAE]=0.317; root relative squared error [RRSE]=0.410; relative absolute error [RAE]=0.399; normalized RMSE [NRMSE]=0.040; mean absolute deviation [MAD]=0.031) and (2) RSDC (MAPE=3.171%; RMSE=0.421; MAE=0.321; RRSE=0.407; RAE=0.393; NRMSE=0.041; MAD=0.031). The performance obtained for MLP-based deep neural networks are as follows: (1) RADC (MAPE=3.103%; RMSE=0.413; MAE=0.316; RRSE=0.410; RAE=0.397; NRMSE=0.040; MAD=0.031) and (2) RSDC (MAPE=3.202%; RMSE=0.427; MAE=0.326; RRSE=0.413; RAE=0.399; NRMSE=0.041; MAD=0.032). Repeated measures ANOVA revealed that the mean RMSE differed significantly across models with P<.001. Post hoc tests using the Bonferroni correction method indicated that the mean RMSE of the deep learning/XGBoost models was statistically significantly (P<.001) lower than that of all other models, namely linear regression/elastic net/lasso/ridge regression.ConclusionsModels built using XGBoost and MLP are suitable for predicting readmission charges billed by hospitals. The MDCs allow models to accurately predict hospital readmission charges.

Project description:BackgroundThe V. parahaemolyticus pandemic clone, results in the development of gastrointestinal illness in humans. Toxigenic strains of this species are frequently isolated from aquatic habitats and organisms such as mollusks and crustaceans. Reports on the isolation of the pandemic clone started in 1996, when a new O3:K6 clone was identified in Asia, that rapidly spread worldwide, becoming the predominant clone isolated from clinical cases. In this study whole genome sequencing was accomplished with an Illumina MiniSeq platform, upon six novel V. parahaemolyticus strains, that have been isolated in Mexico since 1998 and three representative genomes of strains that were isolated from reported outbreaks in other American countries, and were deposited in the GenBank. These nine genomes were compared against the reference sequence of the O3:K6 pandemic strain (RIMD 2210633), which was isolated in 1996, to determine sequence differences within American isolates and between years of isolation.ResultsThe results indicated that strains that were isolated at different times and from different countries, were highly genetically similar, among them as well as to the reference strain RIMD 2210633, indicating a high level of genetic stability among the strains from American countries between 1996 to 2012, without significant genetic changes relative to the reference strain RIMD 2210633, which was isolated in 1996 and was considered to be representative of a novel O3:K6 pandemic strain.ConclusionsThe genomes of V. parahaemolyticus strains isolated from clinical and environmental sources in Mexico and other American countries, presented common characteristics that have been reported for RIMD 2210633 O3:K6 pandemic strain. The major variations that were registered in this study corresponded to genes non associated to virulence factors, which could be the result of adaptations to different environmental conditions. Nevertheless, results do not show a clear pattern with the year or locality where the strains were isolated, which is an indication of a genomic stability of the studied strains.

Dataset Information

Assessing the Complex and Evolving Relationship between Charges and Payments in US Hospitals: 1996 - 2012.

Background

Methods

Results

Conclusions

Publications

Assessing the Complex and Evolving Relationship between Charges and Payments in US Hospitals: 1996 - 2012.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets